Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Zavrtanik, Vitjan"'
Low-shot object counters estimate the number of objects in an image using few or no annotated exemplars. Objects are localized by matching them to prototypes, which are constructed by unsupervised image-wide object appearance aggregation. Due to pote
Externí odkaz:
http://arxiv.org/abs/2409.18686
Low-shot counters estimate the number of objects corresponding to a selected category, based on only few or no exemplars annotated in the image. The current state-of-the-art estimates the total counts as the sum over the object location density map,
Externí odkaz:
http://arxiv.org/abs/2404.16622
Surface anomaly detection is a vital component in manufacturing inspection. Current discriminative methods follow a two-stage architecture composed of a reconstructive network followed by a discriminative network that relies on the reconstruction out
Externí odkaz:
http://arxiv.org/abs/2311.09999
RGB-based surface anomaly detection methods have advanced significantly. However, certain surface anomalies remain practically invisible in RGB alone, necessitating the incorporation of 3D information. Existing approaches that employ point-cloud back
Externí odkaz:
http://arxiv.org/abs/2311.01117
We consider low-shot counting of arbitrary semantic categories in the image using only few annotated exemplars (few-shot) or no exemplars (no-shot). The standard few-shot pipeline follows extraction of appearance queries from exemplars and matching t
Externí odkaz:
http://arxiv.org/abs/2211.08217
The state-of-the-art in discriminative unsupervised surface anomaly detection relies on external datasets for synthesizing anomaly-augmented training images. Such approaches are prone to failure on near-in-distribution anomalies since these are diffi
Externí odkaz:
http://arxiv.org/abs/2208.01521
Visual surface anomaly detection aims to detect local image regions that significantly deviate from normal appearance. Recent surface anomaly detection methods rely on generative models to accurately reconstruct the normal areas and to fail on anomal
Externí odkaz:
http://arxiv.org/abs/2108.07610
Publikováno v:
In Pattern Recognition Letters May 2024 181:113-119
Publikováno v:
In Pattern Recognition April 2021 112
Publikováno v:
In Engineering Applications of Artificial Intelligence February 2020 88